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A robust technique for copy-move forgery detection from small and extremely smooth tampered regions based on the DHE-SURF features and mDBSCAN clustering

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AUSTRALIAN JOURNAL OF FORENSIC SCIENCES 2021年第4期53卷 459-482页

作者： Bilal, Muhammad Habib, Hafiz Adnan Mehmood, Zahid Yousaf, Rehan Mehmood Saba, Tanzila Rehman, Amjad Univ Engn & Technol Dept Software Engn Taxila Pakistan Univ Engn & Technol Dept Comp Engn Taxila Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh Saudi Arabia

The keypoint-based copy-move forgery (CMF) detection is one of the most widely used CMF detection methods;however the keypoint-based CMF detection method cannot effectively and efficiently detect the small and extremely smooth tampered regions in the input image. A CMF detection method is proposed to tackle the above mention problem. In the proposed CMF detection method, the contrast of the input image is adjusted using the dynamic histogram equalization (DHE) method. A speeded-up robust feature (SURF) descriptor is used to extract features from the tampered image and matched using Euclidean distance. The novel modified density-based spatial clustering of application with noise (mDBSCAN) clustering technique is applied to the matched features to generate the binary mask followed by the detection of CMF regions. Three standard datasets, MICC-F220, MICC-F2000, and CoMoFoD, are used to evaluate the proposed CMF detection method performance. The experimental results indicate that the proposed CMF detection method outshines the state-of-the-art CMF detection method in terms of precision (P) and recall (R).

关键词： CMF detection method SURF features DBSCAN clustering

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Reduced Reference Image Quality Assessment Technique Based on DWT and Path Integral Local Binary Patterns

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第4期45卷 3387-3401页

作者： Abbas, Naveed Saba, Tanzila Khan, Siraj Mehmood, Zahid Rehman, Amjad Tabasum, Rubby Islamia Coll Univ Dept Comp Sci Peshawar Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Riyadh Saudi Arabia Univ Engn & Technol Dept Comp Engn Taxila 47050 Pakistan

In the prevailing technological advancements, image and video services are one of the primary concerns of researches around the world. The transmission of multimedia contents involved various types of attenuations such as blurring, ringing, contrast, and blocking. To evaluate the quality of the image or video, researchers presented various quality assessment techniques, including subjective and objective assessments. In this article, a robust reduced reference image quality assessment technique based on the objective assessment is presented, which considers the extracted features of the underlying image to evaluate its quality. The proposed method uses a discrete wavelet transform-based path integral local binary pattern histogram as extracted features. These features of the reference and testing image are compared by a Euclidean distance formula to generate a single value to evaluate the quality of the image. The qualitative and quantitative analysis of the proposed method is carried on three different image datasets, namely LIVE, TID2013, and CSIQ. The experimental results of the proposed method indicate that it produces robust performance in terms of performance evaluation metrics as compared with state-of-the-art methods.

关键词： Reduced reference image quality assessment Discrete wavelet transform Path integral local binary patterns RMSE

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Volumetric Segmentation of Brain Regions From MRI Scans Using 3D Convolutional Neural Networks

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IEEE ACCESS 2020年 8卷 103697-103709页

作者： Ramzan, Farheen Khan, Muhammad Usman Ghani Iqbal, Sajid Saba, Tanzila Rehman, Amjad Univ Engn & Technol UET Lahore Natl Ctr Artificial Intelligence Al Khawarizmi Inst Comp Sci KICS Intelligent Criminol Res Lab Lahore 54000 Pakistan Bahauddin Zakariya Univ Dept Comp Sci Multan 60700 Pakistan Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia

Automated brain segmentation is an active research domain due to the association of various neurological disorders with different regions of the brain, to help medical professionals in prognostics and diagnostics. Traditional techniques like atlas-based and pattern recognition-based methods led to the development of various tools for automated brain segmentation. Recently, deep learning techniques are outperforming classical state-of-the-art methods and gradually becoming more mature. Consequently, deep learning has been extensively employed as a tool for precise segmentation of brain regions because of its capability to learn the intricate features of the high-dimensional data. In this work, a network for the segmentation of multiple brain regions has been proposed that is based on 3D convolutional neural networks and utilizes residual learning and dilated convolution operations to efficiently learn the end-to-end mapping from MRI volumes to the voxel-level brain segments. This research is focused on the segmentation of up to nine brain regions including cerebrospinal fluid, white matter and gray matter as well as their sub-regions. Mean dice scores of 0.879 and 0.914 have been achieved for three and nine brain regions, respectively by using the data from three different sources. Comparative analysis shows that our network gives better dice scores for most of the brain regions than state-of-the-artwork. Moreover, the mean dice score of 0.903, obtained for eight brain regions segmentation with MRBrains18 dataset, is better than 0.876 which was achieved in the previous work.

关键词： Brain segmentation convolutional neural networks magnetic resonance imaging volumetric segmentation

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Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques

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Computers, Materials & Continua 2024年第4期79卷 651-673页

作者： Ahsan Wajahat Jingsha He Nafei Zhu Tariq Mahmood Tanzila Saba Amjad Rehman Khan Faten S.A.lamri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy *** escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital *** methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false *** meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in *** proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data *** Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction *** study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert *** proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,*** empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both *** app

关键词： Android malware detection machine learning SVC K-Nearest Neighbors(KNN) RF

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Scene Graph Generation With Structured Aspect of Segmenting the Big Distributed Clusters

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IEEE ACCESS 2022年 10卷 24264-24272页

作者： Khan, Amjad Rehman Mukhtar, Hamza Saba, Tanzila Riaz, Omer Khan, Muhammad Usman Ghani Bahaj, Saeed Ali Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia UET Lahore Natl Ctr Artificial Intelligence KICS Lahore 54000 Pakistan Islamic Univ Bahawalpur Fac Comp Dept Informat Technol Bahawalpur 63100 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 11942 Saudi Arabia

Accurate fruit counting is one of the significant phenotypic traits for crucial fruit harvesting decision making. Existing approaches perform counting through detection or regression-based approaches. Detection of fruit instances is very challenging because of the very small fruit size compared to the whole size image of a tree. At the same time, regression-based counting techniques contributes impressive results but presents inaccurate results while number of instances increases. Moreover, most approaches lack scalability and are applicable only on one or two fruit types. This paper proposes a fruit counting mechanism that combines loose segmentation and regression counting that works on six fruit types: Apple, Orange, Tomato, Peach, Pomegranate and Almond. Through relaxed segmentation, fruit clusters are segmented to extract the small image regions which contain the small cluster of fruits. Extracted regions are forwarded for the regression counting of fruits. Relaxed segmentation is achieved through a state-of-the-art deconvolutional network, while modified Inception Residual Networks (ResNet) based nonlinear regression module is proposed for fruit counting. For segmentation, 4,820 original images, including corresponding mask images, of all six fruit types are augmented to 32,412 images through different augmentation techniques, while 21,450 extracted patches are augmented to 89,120 images used for the regression module training. The proposed approach attained a counting accuracy of 94.71% for individual fruit types higher than techniques reported in literature.

关键词： Image segmentation Feature extraction Motion segmentation Deep learning Location awareness Yield estimation Training Deep learning segmentation fruit counting agricultural yield estimation economic growth agriculture technological development

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Sensors-Enabled Autonomous Computational intelligence Vehicle Model With QoS-Driven Communication Services

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IEEE SENSORS JOURNAL 2024年第16期24卷 26607-26615页

作者： Haseeb, Khalid Rehman, Amjad Ara, Anees Khan, Atif Saba, Tanzila Song, Houbing Herbert Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh 12435 Saudi Arabia Islamia Coll Peshawar Dept Comp Sci Peshawar 25120 Pakistan Univ Maryland Baltimore Cty Dept Informat Syst Baltimore MD 21250 USA

Edge computing is integrated with various physical and computational components in autonomous networks to develop transportation systems. These systems can gather data from the intended field and send it to computing servers for data analysis and sharing with network users. The integration of vehicles with sensor technology improves the automotive industry for safety and road monitoring. However, load balancing and communication stability represent some of the major research challenges for vehicle communication because of the inherent limitations of sensor technologies in emerging networks. Most of the solutions have been presented based on centralized computing, which effectively increases the data flow;nonetheless, distributed channels and heterogeneous devices continue to pose significant quality of services issues. Such problems lower the optimization criteria and reflect communication structure around the network edges. This research presents an autonomous computing model with mobile edge technology and a cooperative intelligent transportation network algorithm. First, mobile data collectors are identified with computational intelligence to forward the relaying data and balance the traffic overhead. Second, edged-enabled authentic procedures are defined for improving integrity and privacy in a trust-lacking environment. In terms of network throughput, data latency, network lifetime, and retransmission load under varying data rates and vehicle sensor scenarios, the significance of the proposed algorithm is assessed, and the performance results demonstrate that it outperforms the state-of-the-art schemes.

关键词： Transportation Routing Costs Routing protocols Quality of service Mathematical models Vehicle dynamics Distributed intelligence innovative process optimization quality of service transportation systems

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Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition

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Computers, Materials & Continua 2023年第2期74卷 4135-4147页

作者： Zaid Nidhal Khudhair Farhan Mohamed Amjad Rehman Tanzila Saba Saeed Ali bahaj Faculty of Engineering School of ComputingUniversity Technology of MalaysiaJohar BahruMalaysia UTM-IRDA MaGICX Institute of Human Centered EngineeringUniversiti Teknologi MalaysiaMalaysia Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan University Riyadh Saudi Arabia Computer Techniques Engineering Department Faculty of Information TechnologyImam Ja’afar Al-Sadiq UniversityBaghdadIraq

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined *** each step,the SVD is ***,the diagonal matrix’s maximum value(norm)is selected(representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled).Then,the similar norms are grouped,and each leading group is separated into many subgroups(elements of each subgroup are neighbors)according to 8-adjacency(the subgroups for each leading group must be far from others by a specific distance).After that,a weight is assigned for each subgroup to classify the image as forgery or ***,the F1 score of the proposed system is measured,reaching 99.1%.This approach is robust against rotation,scaling,noisy images,and illumination *** is compared with other similarmethods and presents very promised results.

关键词： Forgery image forensic image processing region duplication SVD transformation technological development

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Finger Vein Authentication Based on Wavelet Scattering Networks

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Computers, Materials & Continua 2022年第8期72卷 3369-3383页

作者： Amjad Rehman Majid Harouni Maedeh Omidiravesh Suliman Mohamed Fati Saeed Ali Bahaj Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering Dolatabad BranchIslamic Azad UniversityIsfahanIran Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityIsfahanIran MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two *** biometrics such as fingerprint,palm,iris,palm vein and finger vein and other biometrics have been *** of the challenges in biometrics is physical *** of finger vein is of the biometrics least exposed to physical *** methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification *** paper presents a novel method of scattering wavelet-based identity *** wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural *** distinguishes this algorithm from other popular feature extraction methods such as deep learning methods,filter-based methods,statistical methods,etc.,is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes,even when the image is subject to serious damage such as noise,angle changes or pixel location,this descriptor still generates feature vectors in away thatminimizes classifier *** improves classification and *** proposed method has been evaluated using two databases Finger Vein USM(FV-USM)and Homologous Multimodal biometrics Traits(SDUMLA-HMT).In addition to having reasonable computational complexity,it has recorded excellent identification rates in noise,rotation,and transmission *** best,it has a 98.2%identification rate for the SDUMLA-HMT database and a 96.1%identification rate for the FV-USM database.

关键词： Biometrics finger veins wavelet scattering vein authentication disaster risk reduction

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Empirical enhancement of EEG classification models for epileptic seizures using a fuzzy statistical perspective

Empirical enhancement of EEG classification models for epile...

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作者： Ricky Mohanty Subhendu Kumar Pani Amjad Rehman Noor Ayesha Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University School of Clinical Medicine Zhengzhou University

The signals produced by an electrocardiogram(ECG) are made up of intricate pattern sequences that have a periodic *** pattern sequences contain an initial P-wave,which denotes the beginning of an ECG wave,a QRS sequence,which denotes the intensity of the pulse,and a T segment,which denotes the conclusion of the *** such as PR interval,QRS interval,QT interval,ST interval,R to R interval,*** employed to recognize chronic,ischemic,and other cardiac *** wave patterns need the simultaneous execution of many high-complexity signal processing operations to be classified into cardiac *** pre-processing,feature extraction,feature selection,classification into epileptic and non-epileptic seizures,and post-processing are some of these *** each of these operations,researchers create a wide range of *** algorithms' performance differs significantly in terms of the quantity of leads utilized for ECG collection,filtering effectiveness,feature extraction & selection effectiveness,and classifier ***,researchers and system designers have become unclear when choosing the optimum algorithm set for an *** text offers a thorough analysis & design of fuzzy CNN model of a broad range of epileptic & non-epileptic seizure classification techniques to lessen *** neural network(CNN) based models outperform other models in terms of general-purpose performance,whereas application-specific deployments need the employment of customized fuzzy CNN models.A significant area of research & development is presented by the observation that fuzzy logic techniques are not observed while constructing ECG classification *** on these findings,a new fuzzy logic-based classification method is provided in this text that employs quantization techniques to transform input ECG signals into fuzzy *** these parameters and a specially created CNN model,it was possible to a

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Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current analytics and Comparisons

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IT PROFESSIONAL 2021年第3期23卷 63-67页

作者： Rehman, Amjad Saba, Tanzila Tariq, Usman Ayesha, Noor CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Riyadh Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj Saudi Arabia Zhengzhou Univ Sch Clin Med Zhengzhou Peoples R China

Currently, the world faces a novel coronavirus disease 2019 (COVID-19) challenge and infected cases are increasing exponentially. COVID-19 is a disease that has been reported by the WHO in March 2020, caused by a virus called the SARS-CoV-2. As of 10 March 2021, more than 150 million people were infected and 3v million died. Researchers strive to find out about the virus and recommend effective actions. An unprecedented increase in pathogens is happening and a major attempt is being made to tackle the epidemic. This article presents deep learning-based COVID-19 detection using CT and X-ray images and data analytics on its spread worldwide. This article's research structure builds on a recent analysis of the COVID-19 data and prospective research to systematize current resources, help the researchers, practitioners by using in-depth learning methodologies to build solutions for the COVID-19 pandemic.

关键词： COVID-19 Deep learning Pathogens Pandemics Computed tomography data science Market research

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